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Defect detection of automotive injector valve seat based on feature fusion (part 2)

Although the Faster R-CNN algorithm has good detection performance in the detection of objects, the seat defect size of automobile fuel injector is relatively small and there are many kinds of defects. Therefore, the Faster R-CNN detection is used in the process, it is impossible to accurately complete the identification and positioning of defects, which is likely to cause a missed inspection. In this paper, we introduce the idea of feature fusion on the Faster R-CNN algorithm, fuse the features of different convolution layers, improve the expression ability of the detection algorithm, and make it more accurate to detect the defects of the valve seat of the automobile injector.

2. Dataset Construction

2.1 Image Data Processing

In the process of collecting defects in the valve seat of the automobile injector with the help of hardware such as CCD industrial cameras, tooling, PC, etc., due to the interference of the environment, current, operation and other factors, the collected pictures will increase the difficulty of subsequent operations, in order to simplify Subsequent work requires effective methods to preprocess the images in actual production.

First, during the image acquisition process, there will be problems such as image redundancy and naming irregularities during saving. Redundant images will not only affect the work the efficiency has a great impact, and it will increase the difficulty of subsequent work. Therefore, it is necessary to remove duplicate pictures.

Secondly, in the collection In the process of the picture, due to the influence of current and noise, some irrelevant information will be generated. Therefore, it is necessary to use the Gaussian filtering method to denoise the image and retain the useful information for detection and recognition.